Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations547271
Missing cells629315
Missing cells (%)5.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory87.7 MiB
Average record size in memory168.0 B

Variable types

DateTime1
Categorical4
Numeric12
Text4

Alerts

ARR_DELAY is highly overall correlated with CANCELLED and 2 other fieldsHigh correlation
ARR_TIME is highly overall correlated with CANCELLED and 1 other fieldsHigh correlation
CANCELLATION_CODE is highly overall correlated with CANCELLED and 2 other fieldsHigh correlation
CANCELLED is highly overall correlated with ARR_DELAY and 2 other fieldsHigh correlation
DEP_DELAY is highly overall correlated with ARR_DELAYHigh correlation
DEP_TIME is highly overall correlated with ARR_TIMEHigh correlation
DEST_AIRPORT_ID is highly overall correlated with DEST_AIRPORT_SEQ_ID and 1 other fieldsHigh correlation
DEST_AIRPORT_SEQ_ID is highly overall correlated with DEST_AIRPORT_ID and 1 other fieldsHigh correlation
DEST_CITY_MARKET_ID is highly overall correlated with DEST_AIRPORT_ID and 1 other fieldsHigh correlation
DIVERTED is highly overall correlated with ARR_DELAY and 1 other fieldsHigh correlation
OP_UNIQUE_CARRIER is highly overall correlated with CANCELLATION_CODEHigh correlation
ORIGIN_AIRPORT_ID is highly overall correlated with ORIGIN_AIRPORT_SEQ_ID and 1 other fieldsHigh correlation
ORIGIN_AIRPORT_SEQ_ID is highly overall correlated with ORIGIN_AIRPORT_ID and 1 other fieldsHigh correlation
ORIGIN_CITY_MARKET_ID is highly overall correlated with ORIGIN_AIRPORT_ID and 1 other fieldsHigh correlation
CANCELLED is highly imbalanced (77.0%)Imbalance
DIVERTED is highly imbalanced (97.3%)Imbalance
DEP_TIME has 19784 (3.6%) missing valuesMissing
DEP_DELAY has 19858 (3.6%) missing valuesMissing
TAXI_OUT has 20257 (3.7%) missing valuesMissing
ARR_TIME has 20633 (3.8%) missing valuesMissing
ARR_DELAY has 21901 (4.0%) missing valuesMissing
CANCELLATION_CODE has 526882 (96.3%) missing valuesMissing
DEP_DELAY has 23234 (4.2%) zerosZeros
ARR_DELAY has 8971 (1.6%) zerosZeros

Reproduction

Analysis started2024-09-18 02:39:36.512949
Analysis finished2024-09-18 02:40:18.212898
Duration41.7 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Minimum2024-01-01 00:00:00
Maximum2024-01-31 00:00:00
2024-09-17T20:40:18.307953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:18.462543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)

OP_UNIQUE_CARRIER
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
WN
115389 
AA
77346 
DL
74384 
UA
58855 
OO
56814 
Other values (10)
164483 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1094542
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9E
2nd row9E
3rd row9E
4th row9E
5th row9E

Common Values

ValueCountFrequency (%)
WN 115389
21.1%
AA 77346
14.1%
DL 74384
13.6%
UA 58855
10.8%
OO 56814
10.4%
YX 22914
 
4.2%
MQ 20750
 
3.8%
NK 20415
 
3.7%
B6 19580
 
3.6%
AS 17775
 
3.2%
Other values (5) 63049
11.5%

Length

2024-09-17T20:40:18.600173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wn 115389
21.1%
aa 77346
14.1%
dl 74384
13.6%
ua 58855
10.8%
oo 56814
10.4%
yx 22914
 
4.2%
mq 20750
 
3.8%
nk 20415
 
3.7%
b6 19580
 
3.6%
as 17775
 
3.2%
Other values (5) 63049
11.5%

Most occurring characters

ValueCountFrequency (%)
A 237898
21.7%
N 135804
12.4%
O 130154
11.9%
W 115389
10.5%
D 74384
 
6.8%
L 74384
 
6.8%
U 58855
 
5.4%
9 31351
 
2.9%
H 23102
 
2.1%
Y 22914
 
2.1%
Other values (11) 190307
17.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1094542
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 237898
21.7%
N 135804
12.4%
O 130154
11.9%
W 115389
10.5%
D 74384
 
6.8%
L 74384
 
6.8%
U 58855
 
5.4%
9 31351
 
2.9%
H 23102
 
2.1%
Y 22914
 
2.1%
Other values (11) 190307
17.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1094542
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 237898
21.7%
N 135804
12.4%
O 130154
11.9%
W 115389
10.5%
D 74384
 
6.8%
L 74384
 
6.8%
U 58855
 
5.4%
9 31351
 
2.9%
H 23102
 
2.1%
Y 22914
 
2.1%
Other values (11) 190307
17.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1094542
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 237898
21.7%
N 135804
12.4%
O 130154
11.9%
W 115389
10.5%
D 74384
 
6.8%
L 74384
 
6.8%
U 58855
 
5.4%
9 31351
 
2.9%
H 23102
 
2.1%
Y 22914
 
2.1%
Other values (11) 190307
17.4%

OP_CARRIER_FL_NUM
Real number (ℝ)

Distinct5914
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2344.8843
Minimum1
Maximum8819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2024-09-17T20:40:18.745752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile295
Q11083
median2069
Q33454
95-th percentile5384
Maximum8819
Range8818
Interquartile range (IQR)2371

Descriptive statistics

Standard deviation1576.2543
Coefficient of variation (CV)0.67220983
Kurtosis-0.6721993
Mean2344.8843
Median Absolute Deviation (MAD)1148
Skewness0.56529359
Sum1.2832872 × 109
Variance2484577.5
MonotonicityNot monotonic
2024-09-17T20:40:18.907319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
698 295
 
0.1%
1245 275
 
0.1%
687 270
 
< 0.1%
555 266
 
< 0.1%
777 264
 
< 0.1%
321 256
 
< 0.1%
1279 255
 
< 0.1%
336 254
 
< 0.1%
311 253
 
< 0.1%
396 253
 
< 0.1%
Other values (5904) 544630
99.5%
ValueCountFrequency (%)
1 146
< 0.1%
2 122
< 0.1%
3 120
< 0.1%
4 124
< 0.1%
5 106
< 0.1%
6 94
< 0.1%
7 135
< 0.1%
8 98
< 0.1%
9 153
< 0.1%
10 144
< 0.1%
ValueCountFrequency (%)
8819 3
< 0.1%
8818 2
 
< 0.1%
8817 1
 
< 0.1%
8811 2
 
< 0.1%
8810 1
 
< 0.1%
8809 2
 
< 0.1%
8808 3
< 0.1%
8807 1
 
< 0.1%
8806 1
 
< 0.1%
8804 5
< 0.1%

ORIGIN_AIRPORT_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct334
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12659.023
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2024-09-17T20:40:19.173639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1526.2764
Coefficient of variation (CV)0.12056827
Kurtosis-1.2942228
Mean12659.023
Median Absolute Deviation (MAD)1591
Skewness0.1064243
Sum6.9279161 × 109
Variance2329519.8
MonotonicityNot monotonic
2024-09-17T20:40:19.343188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 26315
 
4.8%
11298 23570
 
4.3%
11292 23361
 
4.3%
13930 20321
 
3.7%
11057 16378
 
3.0%
14107 15378
 
2.8%
12892 15228
 
2.8%
12889 14942
 
2.7%
13204 14296
 
2.6%
12953 12700
 
2.3%
Other values (324) 364782
66.7%
ValueCountFrequency (%)
10135 349
 
0.1%
10136 151
 
< 0.1%
10140 1808
0.3%
10141 61
 
< 0.1%
10146 62
 
< 0.1%
10155 93
 
< 0.1%
10157 148
 
< 0.1%
10158 262
 
< 0.1%
10165 9
 
< 0.1%
10170 56
 
< 0.1%
ValueCountFrequency (%)
16869 149
 
< 0.1%
16218 111
 
< 0.1%
15991 60
 
< 0.1%
15919 979
0.2%
15841 60
 
< 0.1%
15624 553
0.1%
15607 62
 
< 0.1%
15582 52
 
< 0.1%
15569 53
 
< 0.1%
15412 1117
0.2%

ORIGIN_AIRPORT_SEQ_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct334
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1265906.2
Minimum1013506
Maximum1686902
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2024-09-17T20:40:19.508737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1013506
5-th percentile1039707
Q11129202
median1288904
Q31402702
95-th percentile1489302
Maximum1686902
Range673396
Interquartile range (IQR)273500

Descriptive statistics

Standard deviation152627.43
Coefficient of variation (CV)0.12056772
Kurtosis-1.2942287
Mean1265906.2
Median Absolute Deviation (MAD)159098
Skewness0.10642538
Sum6.9279377 × 1011
Variance2.3295134 × 1010
MonotonicityNot monotonic
2024-09-17T20:40:19.677293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1039707 26315
 
4.8%
1129806 23570
 
4.3%
1129202 23361
 
4.3%
1393008 20321
 
3.7%
1105703 16378
 
3.0%
1410702 15378
 
2.8%
1289208 15228
 
2.8%
1288904 14942
 
2.7%
1320402 14296
 
2.6%
1295304 12700
 
2.3%
Other values (324) 364782
66.7%
ValueCountFrequency (%)
1013506 349
 
0.1%
1013603 151
 
< 0.1%
1014005 1808
0.3%
1014106 61
 
< 0.1%
1014602 62
 
< 0.1%
1015502 93
 
< 0.1%
1015706 148
 
< 0.1%
1015804 262
 
< 0.1%
1016506 9
 
< 0.1%
1017004 56
 
< 0.1%
ValueCountFrequency (%)
1686902 149
 
< 0.1%
1621802 111
 
< 0.1%
1599102 60
 
< 0.1%
1591905 979
0.2%
1584102 60
 
< 0.1%
1562404 553
0.1%
1560702 62
 
< 0.1%
1558203 52
 
< 0.1%
1556903 53
 
< 0.1%
1541206 1117
0.2%

ORIGIN_CITY_MARKET_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct311
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31751.846
Minimum30070
Maximum35991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2024-09-17T20:40:19.843846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum30070
5-th percentile30194
Q130647
median31454
Q332467
95-th percentile34570
Maximum35991
Range5921
Interquartile range (IQR)1820

Descriptive statistics

Standard deviation1320.5635
Coefficient of variation (CV)0.041590133
Kurtosis-0.2889393
Mean31751.846
Median Absolute Deviation (MAD)994
Skewness0.80497411
Sum1.7376865 × 1010
Variance1743888
MonotonicityNot monotonic
2024-09-17T20:40:20.006412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31703 33930
 
6.2%
30194 29607
 
5.4%
30397 26315
 
4.8%
30977 26207
 
4.8%
32575 24575
 
4.5%
30325 23361
 
4.3%
30852 23025
 
4.2%
32467 18674
 
3.4%
32457 17503
 
3.2%
31057 16378
 
3.0%
Other values (301) 307696
56.2%
ValueCountFrequency (%)
30070 56
 
< 0.1%
30073 46
 
< 0.1%
30107 30
 
< 0.1%
30113 60
 
< 0.1%
30135 349
 
0.1%
30136 151
 
< 0.1%
30140 1808
0.3%
30141 61
 
< 0.1%
30146 62
 
< 0.1%
30155 93
 
< 0.1%
ValueCountFrequency (%)
35991 60
 
< 0.1%
35841 60
 
< 0.1%
35582 52
 
< 0.1%
35569 53
 
< 0.1%
35550 62
 
< 0.1%
35412 1117
0.2%
35411 93
 
< 0.1%
35401 69
 
< 0.1%
35389 62
 
< 0.1%
35380 211
 
< 0.1%

ORIGIN
Text

Distinct334
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2024-09-17T20:40:20.472467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1641813
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJFK
2nd rowMSP
3rd rowJFK
4th rowRIC
5th rowDTW
ValueCountFrequency (%)
atl 26315
 
4.8%
dfw 23570
 
4.3%
den 23361
 
4.3%
ord 20321
 
3.7%
clt 16378
 
3.0%
phx 15378
 
2.8%
lax 15228
 
2.8%
las 14942
 
2.7%
mco 14296
 
2.6%
lga 12700
 
2.3%
Other values (324) 364782
66.7%
2024-09-17T20:40:21.027981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 186539
 
11.4%
L 153698
 
9.4%
S 139156
 
8.5%
D 127493
 
7.8%
T 87421
 
5.3%
C 84060
 
5.1%
O 82470
 
5.0%
M 74361
 
4.5%
F 67709
 
4.1%
W 63837
 
3.9%
Other values (16) 575069
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1641813
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 186539
 
11.4%
L 153698
 
9.4%
S 139156
 
8.5%
D 127493
 
7.8%
T 87421
 
5.3%
C 84060
 
5.1%
O 82470
 
5.0%
M 74361
 
4.5%
F 67709
 
4.1%
W 63837
 
3.9%
Other values (16) 575069
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1641813
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 186539
 
11.4%
L 153698
 
9.4%
S 139156
 
8.5%
D 127493
 
7.8%
T 87421
 
5.3%
C 84060
 
5.1%
O 82470
 
5.0%
M 74361
 
4.5%
F 67709
 
4.1%
W 63837
 
3.9%
Other values (16) 575069
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1641813
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 186539
 
11.4%
L 153698
 
9.4%
S 139156
 
8.5%
D 127493
 
7.8%
T 87421
 
5.3%
C 84060
 
5.1%
O 82470
 
5.0%
M 74361
 
4.5%
F 67709
 
4.1%
W 63837
 
3.9%
Other values (16) 575069
35.0%
Distinct328
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2024-09-17T20:40:21.414946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.097882
Min length8

Characters and Unicode

Total characters7168091
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York, NY
2nd rowMinneapolis, MN
3rd rowNew York, NY
4th rowRichmond, VA
5th rowDetroit, MI
ValueCountFrequency (%)
tx 58037
 
4.5%
ca 57574
 
4.5%
fl 56092
 
4.4%
ny 28471
 
2.2%
ga 28164
 
2.2%
san 27793
 
2.2%
co 27365
 
2.1%
il 27323
 
2.1%
atlanta 26315
 
2.1%
new 26236
 
2.0%
Other values (398) 917191
71.6%
2024-09-17T20:40:21.955546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
733290
 
10.2%
a 551247
 
7.7%
, 547271
 
7.6%
o 393065
 
5.5%
e 378791
 
5.3%
n 349775
 
4.9%
t 341475
 
4.8%
l 317301
 
4.4%
i 271204
 
3.8%
r 261354
 
3.6%
Other values (46) 3023318
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7168091
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
733290
 
10.2%
a 551247
 
7.7%
, 547271
 
7.6%
o 393065
 
5.5%
e 378791
 
5.3%
n 349775
 
4.9%
t 341475
 
4.8%
l 317301
 
4.4%
i 271204
 
3.8%
r 261354
 
3.6%
Other values (46) 3023318
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7168091
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
733290
 
10.2%
a 551247
 
7.7%
, 547271
 
7.6%
o 393065
 
5.5%
e 378791
 
5.3%
n 349775
 
4.9%
t 341475
 
4.8%
l 317301
 
4.4%
i 271204
 
3.8%
r 261354
 
3.6%
Other values (46) 3023318
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7168091
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
733290
 
10.2%
a 551247
 
7.7%
, 547271
 
7.6%
o 393065
 
5.5%
e 378791
 
5.3%
n 349775
 
4.9%
t 341475
 
4.8%
l 317301
 
4.4%
i 271204
 
3.8%
r 261354
 
3.6%
Other values (46) 3023318
42.2%

DEST_AIRPORT_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct334
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12659.086
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2024-09-17T20:40:22.108173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1526.236
Coefficient of variation (CV)0.12056447
Kurtosis-1.2941919
Mean12659.086
Median Absolute Deviation (MAD)1591
Skewness0.10643995
Sum6.9279507 × 109
Variance2329396.3
MonotonicityNot monotonic
2024-09-17T20:40:22.272698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 26294
 
4.8%
11298 23588
 
4.3%
11292 23357
 
4.3%
13930 20327
 
3.7%
11057 16382
 
3.0%
14107 15367
 
2.8%
12892 15220
 
2.8%
12889 14943
 
2.7%
13204 14304
 
2.6%
12953 12718
 
2.3%
Other values (324) 364771
66.7%
ValueCountFrequency (%)
10135 349
 
0.1%
10136 151
 
< 0.1%
10140 1807
0.3%
10141 61
 
< 0.1%
10146 62
 
< 0.1%
10155 93
 
< 0.1%
10157 149
 
< 0.1%
10158 262
 
< 0.1%
10165 9
 
< 0.1%
10170 57
 
< 0.1%
ValueCountFrequency (%)
16869 149
 
< 0.1%
16218 111
 
< 0.1%
15991 60
 
< 0.1%
15919 977
0.2%
15841 60
 
< 0.1%
15624 553
0.1%
15607 62
 
< 0.1%
15582 52
 
< 0.1%
15569 53
 
< 0.1%
15412 1119
0.2%

DEST_AIRPORT_SEQ_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct334
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1265912.6
Minimum1013506
Maximum1686902
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2024-09-17T20:40:22.438256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1013506
5-th percentile1039707
Q11129202
median1288904
Q31402702
95-th percentile1489302
Maximum1686902
Range673396
Interquartile range (IQR)273500

Descriptive statistics

Standard deviation152623.39
Coefficient of variation (CV)0.12056393
Kurtosis-1.2941977
Mean1265912.6
Median Absolute Deviation (MAD)159098
Skewness0.10644102
Sum6.9279723 × 1011
Variance2.3293899 × 1010
MonotonicityNot monotonic
2024-09-17T20:40:22.607802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1039707 26294
 
4.8%
1129806 23588
 
4.3%
1129202 23357
 
4.3%
1393008 20327
 
3.7%
1105703 16382
 
3.0%
1410702 15367
 
2.8%
1289208 15220
 
2.8%
1288904 14943
 
2.7%
1320402 14304
 
2.6%
1295304 12718
 
2.3%
Other values (324) 364771
66.7%
ValueCountFrequency (%)
1013506 349
 
0.1%
1013603 151
 
< 0.1%
1014005 1807
0.3%
1014106 61
 
< 0.1%
1014602 62
 
< 0.1%
1015502 93
 
< 0.1%
1015706 149
 
< 0.1%
1015804 262
 
< 0.1%
1016506 9
 
< 0.1%
1017004 57
 
< 0.1%
ValueCountFrequency (%)
1686902 149
 
< 0.1%
1621802 111
 
< 0.1%
1599102 60
 
< 0.1%
1591905 977
0.2%
1584102 60
 
< 0.1%
1562404 553
0.1%
1560702 62
 
< 0.1%
1558203 52
 
< 0.1%
1556903 53
 
< 0.1%
1541206 1119
0.2%

DEST_CITY_MARKET_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct311
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31751.864
Minimum30070
Maximum35991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2024-09-17T20:40:22.773360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum30070
5-th percentile30194
Q130647
median31454
Q332467
95-th percentile34570
Maximum35991
Range5921
Interquartile range (IQR)1820

Descriptive statistics

Standard deviation1320.5728
Coefficient of variation (CV)0.041590402
Kurtosis-0.28881987
Mean31751.864
Median Absolute Deviation (MAD)994
Skewness0.80502134
Sum1.7376874 × 1010
Variance1743912.5
MonotonicityNot monotonic
2024-09-17T20:40:22.936922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31703 33949
 
6.2%
30194 29625
 
5.4%
30397 26294
 
4.8%
30977 26209
 
4.8%
32575 24564
 
4.5%
30325 23357
 
4.3%
30852 23027
 
4.2%
32467 18657
 
3.4%
32457 17501
 
3.2%
31057 16382
 
3.0%
Other values (301) 307706
56.2%
ValueCountFrequency (%)
30070 57
 
< 0.1%
30073 46
 
< 0.1%
30107 30
 
< 0.1%
30113 60
 
< 0.1%
30135 349
 
0.1%
30136 151
 
< 0.1%
30140 1807
0.3%
30141 61
 
< 0.1%
30146 62
 
< 0.1%
30155 93
 
< 0.1%
ValueCountFrequency (%)
35991 60
 
< 0.1%
35841 60
 
< 0.1%
35582 52
 
< 0.1%
35569 53
 
< 0.1%
35550 62
 
< 0.1%
35412 1119
0.2%
35411 93
 
< 0.1%
35401 69
 
< 0.1%
35389 62
 
< 0.1%
35380 211
 
< 0.1%

DEST
Text

Distinct334
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2024-09-17T20:40:23.383795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1641813
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDTW
2nd rowCLE
3rd rowRIC
4th rowJFK
5th rowMKE
ValueCountFrequency (%)
atl 26294
 
4.8%
dfw 23588
 
4.3%
den 23357
 
4.3%
ord 20327
 
3.7%
clt 16382
 
3.0%
phx 15367
 
2.8%
lax 15220
 
2.8%
las 14943
 
2.7%
mco 14304
 
2.6%
lga 12718
 
2.3%
Other values (324) 364771
66.7%
2024-09-17T20:40:23.933420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 186546
 
11.4%
L 153656
 
9.4%
S 139145
 
8.5%
D 127527
 
7.8%
T 87412
 
5.3%
C 84075
 
5.1%
O 82470
 
5.0%
M 74356
 
4.5%
F 67709
 
4.1%
W 63854
 
3.9%
Other values (16) 575063
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1641813
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 186546
 
11.4%
L 153656
 
9.4%
S 139145
 
8.5%
D 127527
 
7.8%
T 87412
 
5.3%
C 84075
 
5.1%
O 82470
 
5.0%
M 74356
 
4.5%
F 67709
 
4.1%
W 63854
 
3.9%
Other values (16) 575063
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1641813
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 186546
 
11.4%
L 153656
 
9.4%
S 139145
 
8.5%
D 127527
 
7.8%
T 87412
 
5.3%
C 84075
 
5.1%
O 82470
 
5.0%
M 74356
 
4.5%
F 67709
 
4.1%
W 63854
 
3.9%
Other values (16) 575063
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1641813
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 186546
 
11.4%
L 153656
 
9.4%
S 139145
 
8.5%
D 127527
 
7.8%
T 87412
 
5.3%
C 84075
 
5.1%
O 82470
 
5.0%
M 74356
 
4.5%
F 67709
 
4.1%
W 63854
 
3.9%
Other values (16) 575063
35.0%
Distinct328
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2024-09-17T20:40:24.334349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.09795
Min length8

Characters and Unicode

Total characters7168128
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDetroit, MI
2nd rowCleveland, OH
3rd rowRichmond, VA
4th rowNew York, NY
5th rowMilwaukee, WI
ValueCountFrequency (%)
tx 58057
 
4.5%
ca 57564
 
4.5%
fl 56083
 
4.4%
ny 28494
 
2.2%
ga 28148
 
2.2%
san 27783
 
2.2%
co 27358
 
2.1%
il 27325
 
2.1%
atlanta 26294
 
2.1%
new 26245
 
2.0%
Other values (398) 917203
71.6%
2024-09-17T20:40:24.918784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
733283
 
10.2%
a 551217
 
7.7%
, 547271
 
7.6%
o 393126
 
5.5%
e 378730
 
5.3%
n 349741
 
4.9%
t 341490
 
4.8%
l 317290
 
4.4%
i 271167
 
3.8%
r 261421
 
3.6%
Other values (46) 3023392
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7168128
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
733283
 
10.2%
a 551217
 
7.7%
, 547271
 
7.6%
o 393126
 
5.5%
e 378730
 
5.3%
n 349741
 
4.9%
t 341490
 
4.8%
l 317290
 
4.4%
i 271167
 
3.8%
r 261421
 
3.6%
Other values (46) 3023392
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7168128
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
733283
 
10.2%
a 551217
 
7.7%
, 547271
 
7.6%
o 393126
 
5.5%
e 378730
 
5.3%
n 349741
 
4.9%
t 341490
 
4.8%
l 317290
 
4.4%
i 271167
 
3.8%
r 261421
 
3.6%
Other values (46) 3023392
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7168128
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
733283
 
10.2%
a 551217
 
7.7%
, 547271
 
7.6%
o 393126
 
5.5%
e 378730
 
5.3%
n 349741
 
4.9%
t 341490
 
4.8%
l 317290
 
4.4%
i 271167
 
3.8%
r 261421
 
3.6%
Other values (46) 3023392
42.2%

DEP_TIME
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1424
Distinct (%)0.3%
Missing19784
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean1328.4876
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2024-09-17T20:40:25.086336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile600
Q1914
median1325
Q31738
95-th percentile2127
Maximum2400
Range2399
Interquartile range (IQR)824

Descriptive statistics

Standard deviation498.97377
Coefficient of variation (CV)0.37559535
Kurtosis-0.98257414
Mean1328.4876
Median Absolute Deviation (MAD)412
Skewness0.028119391
Sum7.0075994 × 108
Variance248974.82
MonotonicityNot monotonic
2024-09-17T20:40:25.375594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 1378
 
0.3%
556 1160
 
0.2%
557 1116
 
0.2%
558 1108
 
0.2%
554 1099
 
0.2%
559 1060
 
0.2%
655 1046
 
0.2%
600 1012
 
0.2%
658 988
 
0.2%
553 984
 
0.2%
Other values (1414) 516536
94.4%
(Missing) 19784
 
3.6%
ValueCountFrequency (%)
1 73
< 0.1%
2 54
< 0.1%
3 54
< 0.1%
4 31
< 0.1%
5 44
< 0.1%
6 40
< 0.1%
7 39
< 0.1%
8 34
< 0.1%
9 28
 
< 0.1%
10 44
< 0.1%
ValueCountFrequency (%)
2400 40
 
< 0.1%
2359 91
< 0.1%
2358 82
< 0.1%
2357 83
< 0.1%
2356 79
< 0.1%
2355 101
< 0.1%
2354 114
< 0.1%
2353 103
< 0.1%
2352 103
< 0.1%
2351 101
< 0.1%

DEP_DELAY
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1241
Distinct (%)0.2%
Missing19858
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean15.70068
Minimum-56
Maximum3125
Zeros23234
Zeros (%)4.2%
Negative292317
Negative (%)53.4%
Memory size4.2 MiB
2024-09-17T20:40:25.533142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-56
5-th percentile-10
Q1-5
median-2
Q312
95-th percentile96
Maximum3125
Range3181
Interquartile range (IQR)17

Descriptive statistics

Standard deviation64.175619
Coefficient of variation (CV)4.0874419
Kurtosis207.03893
Mean15.70068
Median Absolute Deviation (MAD)5
Skewness10.777086
Sum8280743
Variance4118.51
MonotonicityNot monotonic
2024-09-17T20:40:25.700694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 36474
 
6.7%
-4 34213
 
6.3%
-3 32723
 
6.0%
-2 30128
 
5.5%
-6 29530
 
5.4%
-1 27006
 
4.9%
-7 25582
 
4.7%
0 23234
 
4.2%
-8 20731
 
3.8%
-10 16343
 
3.0%
Other values (1231) 251449
45.9%
(Missing) 19858
 
3.6%
ValueCountFrequency (%)
-56 1
 
< 0.1%
-47 1
 
< 0.1%
-46 1
 
< 0.1%
-44 1
 
< 0.1%
-43 1
 
< 0.1%
-38 1
 
< 0.1%
-37 3
< 0.1%
-36 4
< 0.1%
-35 2
 
< 0.1%
-34 5
< 0.1%
ValueCountFrequency (%)
3125 1
< 0.1%
2972 1
< 0.1%
2923 1
< 0.1%
2892 1
< 0.1%
2809 1
< 0.1%
2800 1
< 0.1%
2629 1
< 0.1%
2318 1
< 0.1%
2311 1
< 0.1%
2151 1
< 0.1%

TAXI_OUT
Real number (ℝ)

MISSING 

Distinct176
Distinct (%)< 0.1%
Missing20257
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean18.809671
Minimum1
Maximum213
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2024-09-17T20:40:25.867286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q112
median16
Q321
95-th percentile39
Maximum213
Range212
Interquartile range (IQR)9

Descriptive statistics

Standard deviation11.389781
Coefficient of variation (CV)0.60552794
Kurtosis20.951346
Mean18.809671
Median Absolute Deviation (MAD)4
Skewness3.4792089
Sum9912960
Variance129.72712
MonotonicityNot monotonic
2024-09-17T20:40:26.025859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 41318
 
7.5%
12 40117
 
7.3%
14 38954
 
7.1%
15 36324
 
6.6%
11 36298
 
6.6%
16 31845
 
5.8%
10 29060
 
5.3%
17 27978
 
5.1%
18 24323
 
4.4%
19 21038
 
3.8%
Other values (166) 199759
36.5%
(Missing) 20257
 
3.7%
ValueCountFrequency (%)
1 11
 
< 0.1%
2 7
 
< 0.1%
3 49
 
< 0.1%
4 142
 
< 0.1%
5 496
 
0.1%
6 1722
 
0.3%
7 4853
 
0.9%
8 10576
 
1.9%
9 18945
3.5%
10 29060
5.3%
ValueCountFrequency (%)
213 1
 
< 0.1%
210 1
 
< 0.1%
188 1
 
< 0.1%
184 2
< 0.1%
180 1
 
< 0.1%
176 1
 
< 0.1%
171 2
< 0.1%
170 2
< 0.1%
169 4
< 0.1%
167 1
 
< 0.1%

ARR_TIME
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1440
Distinct (%)0.3%
Missing20633
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean1478.9182
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2024-09-17T20:40:26.181439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile705
Q11102
median1513
Q31920
95-th percentile2251
Maximum2400
Range2399
Interquartile range (IQR)818

Descriptive statistics

Standard deviation529.06793
Coefficient of variation (CV)0.35773982
Kurtosis-0.32977478
Mean1478.9182
Median Absolute Deviation (MAD)409
Skewness-0.37510433
Sum7.7885452 × 108
Variance279912.87
MonotonicityNot monotonic
2024-09-17T20:40:26.353947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1638 622
 
0.1%
1656 616
 
0.1%
1645 613
 
0.1%
1643 613
 
0.1%
1633 611
 
0.1%
1728 607
 
0.1%
1634 606
 
0.1%
1738 602
 
0.1%
1740 597
 
0.1%
1446 597
 
0.1%
Other values (1430) 520554
95.1%
(Missing) 20633
 
3.8%
ValueCountFrequency (%)
1 278
0.1%
2 242
< 0.1%
3 285
0.1%
4 229
< 0.1%
5 234
< 0.1%
6 241
< 0.1%
7 241
< 0.1%
8 252
< 0.1%
9 220
< 0.1%
10 219
< 0.1%
ValueCountFrequency (%)
2400 239
< 0.1%
2359 283
0.1%
2358 295
0.1%
2357 289
0.1%
2356 291
0.1%
2355 309
0.1%
2354 317
0.1%
2353 335
0.1%
2352 338
0.1%
2351 386
0.1%

ARR_DELAY
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1282
Distinct (%)0.2%
Missing21901
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean10.352298
Minimum-90
Maximum3136
Zeros8971
Zeros (%)1.6%
Negative306999
Negative (%)56.1%
Memory size4.2 MiB
2024-09-17T20:40:26.521499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-90
5-th percentile-29
Q1-16
median-5
Q313
95-th percentile97
Maximum3136
Range3226
Interquartile range (IQR)29

Descriptive statistics

Standard deviation66.784961
Coefficient of variation (CV)6.4512206
Kurtosis180.88394
Mean10.352298
Median Absolute Deviation (MAD)13
Skewness9.786023
Sum5438787
Variance4460.231
MonotonicityNot monotonic
2024-09-17T20:40:26.687087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-11 12841
 
2.3%
-12 12822
 
2.3%
-13 12661
 
2.3%
-10 12583
 
2.3%
-9 12446
 
2.3%
-8 12421
 
2.3%
-14 12280
 
2.2%
-7 11905
 
2.2%
-15 11851
 
2.2%
-16 11572
 
2.1%
Other values (1272) 401988
73.5%
(Missing) 21901
 
4.0%
ValueCountFrequency (%)
-90 1
 
< 0.1%
-87 1
 
< 0.1%
-86 1
 
< 0.1%
-84 2
 
< 0.1%
-82 1
 
< 0.1%
-81 2
 
< 0.1%
-80 1
 
< 0.1%
-77 4
< 0.1%
-76 4
< 0.1%
-75 5
< 0.1%
ValueCountFrequency (%)
3136 1
< 0.1%
2989 1
< 0.1%
2901 1
< 0.1%
2884 1
< 0.1%
2833 1
< 0.1%
2779 1
< 0.1%
2614 1
< 0.1%
2334 1
< 0.1%
2300 1
< 0.1%
2185 1
< 0.1%

CANCELLED
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
0.0
526882 
1.0
 
20389

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1641813
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 526882
96.3%
1.0 20389
 
3.7%

Length

2024-09-17T20:40:26.833697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-17T20:40:26.953378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 526882
96.3%
1.0 20389
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 1074153
65.4%
. 547271
33.3%
1 20389
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1641813
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1074153
65.4%
. 547271
33.3%
1 20389
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1641813
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1074153
65.4%
. 547271
33.3%
1 20389
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1641813
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1074153
65.4%
. 547271
33.3%
1 20389
 
1.2%

CANCELLATION_CODE
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing526882
Missing (%)96.3%
Memory size4.2 MiB
B
12085 
A
7736 
C
 
568

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20389
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowA
3rd rowA
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
B 12085
 
2.2%
A 7736
 
1.4%
C 568
 
0.1%
(Missing) 526882
96.3%

Length

2024-09-17T20:40:27.066161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-17T20:40:27.183847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
b 12085
59.3%
a 7736
37.9%
c 568
 
2.8%

Most occurring characters

ValueCountFrequency (%)
B 12085
59.3%
A 7736
37.9%
C 568
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20389
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 12085
59.3%
A 7736
37.9%
C 568
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20389
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 12085
59.3%
A 7736
37.9%
C 568
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20389
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 12085
59.3%
A 7736
37.9%
C 568
 
2.8%

DIVERTED
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
0.0
545759 
1.0
 
1512

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1641813
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 545759
99.7%
1.0 1512
 
0.3%

Length

2024-09-17T20:40:27.308514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-17T20:40:27.413234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 545759
99.7%
1.0 1512
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1093030
66.6%
. 547271
33.3%
1 1512
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1641813
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1093030
66.6%
. 547271
33.3%
1 1512
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1641813
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1093030
66.6%
. 547271
33.3%
1 1512
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1641813
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1093030
66.6%
. 547271
33.3%
1 1512
 
0.1%

Interactions

2024-09-17T20:40:13.654020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:49.730828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:51.854227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:54.015524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:56.186727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:58.420752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:00.599926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:02.775108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:05.019077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:07.131428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:09.261731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:11.401011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:13.823564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:49.899455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:52.022773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:54.191031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:56.353283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:58.608251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:00.777451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:02.944660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:05.186660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:07.301973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:09.432276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:11.571555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:14.011062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:50.074977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:52.205287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:54.371581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:56.536792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:58.790763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:00.963952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:03.137140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:05.365184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:07.484484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:09.610798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:11.862776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:14.198561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:50.255503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:52.391787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:54.554092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:56.717309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:58.974273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:01.160426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:03.323636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:05.548661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:07.669023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:09.791348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:12.052269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:14.381073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:50.498860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:52.574303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:54.736611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:56.894833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:59.157782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:01.341941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:03.500171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:05.727183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:07.847514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:09.977817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:12.231789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:14.562587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:50.671391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:52.757810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:54.920120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:57.092275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:59.337304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:01.525450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:03.682658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:05.914682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:08.029060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:10.189251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:12.436243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:14.747096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:50.849910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:52.943312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:55.119550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:57.364578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:59.521808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:01.705967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:03.965894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:06.101183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:08.222510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:10.379741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:12.618754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:14.927613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:51.021455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:53.129815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:55.303091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:57.546094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:59.703327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:01.887489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:04.141455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:06.279706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:08.406020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:10.558265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:12.801298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:15.097158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:51.189009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:53.311332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:55.478621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:57.718630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:59.882813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:02.066971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:04.316985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:06.446294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:08.569613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:10.722857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:12.965859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:15.272691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:51.355557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:53.491847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:55.657144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:57.900151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:00.070341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:02.249516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:04.492516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:06.622789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:08.745141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:10.892371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:13.145346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:15.442235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:51.519120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:53.663444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:55.828684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:58.069699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:00.244875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:02.421056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:04.664057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:06.785354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:08.907709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:11.056931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:13.306945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:15.611782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:51.683684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:53.840004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:56.002222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:39:58.243228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:00.419417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:02.597591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:04.839558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:06.955898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:09.085204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:11.227475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T20:40:13.484471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-17T20:40:27.504988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ARR_DELAYARR_TIMECANCELLATION_CODECANCELLEDDEP_DELAYDEP_TIMEDEST_AIRPORT_IDDEST_AIRPORT_SEQ_IDDEST_CITY_MARKET_IDDIVERTEDOP_CARRIER_FL_NUMOP_UNIQUE_CARRIERORIGIN_AIRPORT_IDORIGIN_AIRPORT_SEQ_IDORIGIN_CITY_MARKET_IDTAXI_OUT
ARR_DELAY1.0000.1170.0001.0000.7080.1540.0030.0030.0041.000-0.0130.020-0.019-0.019-0.0420.287
ARR_TIME0.1171.0000.0001.0000.1500.7600.0210.0210.0450.0330.0330.056-0.014-0.014-0.049-0.028
CANCELLATION_CODE0.0000.0001.0001.0000.0550.1400.2510.2510.2041.0000.3930.6560.2530.2530.2090.074
CANCELLED1.0001.0001.0001.0000.0230.0210.0340.0340.0410.0100.0400.1740.0310.0310.0410.009
DEP_DELAY0.7080.1500.0550.0231.0000.2120.0110.0110.0110.013-0.0650.019-0.040-0.040-0.0740.040
DEP_TIME0.1540.7600.1400.0210.2121.0000.0270.0270.0640.0060.0460.056-0.038-0.038-0.062-0.055
DEST_AIRPORT_ID0.0030.0210.2510.0340.0110.0271.0001.0000.6230.018-0.0270.173-0.004-0.004-0.0240.027
DEST_AIRPORT_SEQ_ID0.0030.0210.2510.0340.0110.0271.0001.0000.6230.018-0.0270.173-0.004-0.004-0.0240.027
DEST_CITY_MARKET_ID0.0040.0450.2040.0410.0110.0640.6230.6231.0000.012-0.0150.169-0.024-0.024-0.0650.029
DIVERTED1.0000.0331.0000.0100.0130.0060.0180.0180.0121.0000.0070.0260.0060.0060.0100.017
OP_CARRIER_FL_NUM-0.0130.0330.3930.040-0.0650.046-0.027-0.027-0.0150.0071.0000.395-0.011-0.0110.0020.113
OP_UNIQUE_CARRIER0.0200.0560.6560.1740.0190.0560.1730.1730.1690.0260.3951.0000.1730.1730.1690.067
ORIGIN_AIRPORT_ID-0.019-0.0140.2530.031-0.040-0.038-0.004-0.004-0.0240.006-0.0110.1731.0001.0000.623-0.025
ORIGIN_AIRPORT_SEQ_ID-0.019-0.0140.2530.031-0.040-0.038-0.004-0.004-0.0240.006-0.0110.1731.0001.0000.623-0.025
ORIGIN_CITY_MARKET_ID-0.042-0.0490.2090.041-0.074-0.062-0.024-0.024-0.0650.0100.0020.1690.6230.6231.000-0.039
TAXI_OUT0.287-0.0280.0740.0090.040-0.0550.0270.0270.0290.0170.1130.067-0.025-0.025-0.0391.000

Missing values

2024-09-17T20:40:15.890007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-17T20:40:16.650005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-17T20:40:17.746177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

FL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGIN_AIRPORT_SEQ_IDORIGIN_CITY_MARKET_IDORIGINORIGIN_CITY_NAMEDEST_AIRPORT_IDDEST_AIRPORT_SEQ_IDDEST_CITY_MARKET_IDDESTDEST_CITY_NAMEDEP_TIMEDEP_DELAYTAXI_OUTARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTED
01/1/2024 12:00:00 AM9E481412478124780531703JFKNew York, NY11433114330231295DTWDetroit, MI1247.0-5.031.01449.0-19.00.0NaN0.0
11/1/2024 12:00:00 AM9E481513487134870231650MSPMinneapolis, MN11042110420530647CLECleveland, OH1001.0-14.020.01255.0-30.00.0NaN0.0
21/1/2024 12:00:00 AM9E481712478124780531703JFKNew York, NY14524145240134524RICRichmond, VA1411.0-4.021.01541.0-20.00.0NaN0.0
31/1/2024 12:00:00 AM9E481714524145240134524RICRichmond, VA12478124780531703JFKNew York, NY1643.0-7.013.01759.0-42.00.0NaN0.0
41/1/2024 12:00:00 AM9E481811433114330231295DTWDetroit, MI13342133420733342MKEMilwaukee, WI1010.0-5.021.01020.0-14.00.0NaN0.0
51/1/2024 12:00:00 AM9E482212451124510231136JAXJacksonville, FL12953129530431703LGANew York, NY1403.0-7.014.01603.0-24.00.0NaN0.0
61/1/2024 12:00:00 AM9E482212953129530431703LGANew York, NY12451124510231136JAXJacksonville, FL947.0-8.026.01231.0-13.00.0NaN0.0
71/1/2024 12:00:00 AM9E482310994109940230994CHSCharleston, SC12953129530431703LGANew York, NY1135.0-5.08.01314.0-24.00.0NaN0.0
81/1/2024 12:00:00 AM9E482312953129530431703LGANew York, NY10994109940230994CHSCharleston, SC810.0-5.014.01013.0-31.00.0NaN0.0
91/1/2024 12:00:00 AM9E482812397123970332397ITHIthaca/Cortland, NY12478124780531703JFKNew York, NY1248.0-12.012.01355.0-24.00.0NaN0.0
FL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGIN_AIRPORT_SEQ_IDORIGIN_CITY_MARKET_IDORIGINORIGIN_CITY_NAMEDEST_AIRPORT_IDDEST_AIRPORT_SEQ_IDDEST_CITY_MARKET_IDDESTDEST_CITY_NAMEDEP_TIMEDEP_DELAYTAXI_OUTARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTED
5472611/31/2024 12:00:00 AMYX583712953129530431703LGANew York, NY13871138710233316OMAOmaha, NE1846.0-9.015.02051.0-41.00.0NaN0.0
5472621/31/2024 12:00:00 AMYX583811433114330231295DTWDetroit, MI14122141220230198PITPittsburgh, PA1615.0-4.028.01728.0-3.00.0NaN0.0
5472631/31/2024 12:00:00 AMYX583814122141220230198PITPittsburgh, PA11433114330231295DTWDetroit, MI1825.0-5.019.01936.0-15.00.0NaN0.0
5472641/31/2024 12:00:00 AMYX584011433114330231295DTWDetroit, MI11066110660631066CMHColumbus, OH1606.0-4.017.01701.0-11.00.0NaN0.0
5472651/31/2024 12:00:00 AMYX584210721107210230721BOSBoston, MA12478124780531703JFKNew York, NY1552.0-8.017.01700.0-25.00.0NaN0.0
5472661/31/2024 12:00:00 AMYX584312953129530431703LGANew York, NY14492144920234492RDURaleigh/Durham, NC1201.051.029.01347.038.00.0NaN0.0
5472671/31/2024 12:00:00 AMYX584412953129530431703LGANew York, NY11278112780530852DCAWashington, DC2016.0-14.016.02128.0-32.00.0NaN0.0
5472681/31/2024 12:00:00 AMYX584510821108210630852BWIBaltimore, MD12478124780531703JFKNew York, NY1719.03.011.01827.0-18.00.0NaN0.0
5472691/31/2024 12:00:00 AMYX584512478124780531703JFKNew York, NY10821108210630852BWIBaltimore, MD1552.031.015.01653.019.00.0NaN0.0
5472701/31/2024 12:00:00 AMYX584615096150960235096SYRSyracuse, NY12953129530431703LGANew York, NY559.0-1.014.0708.0-23.00.0NaN0.0